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Fine-Grained Channel Pruning for Deep Residual Neural Networks Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor

Authors : Thomas Barbier, Céline Teulière, Jochen Triesch

Published in: Artificial Neural Networks and Machine Learning – ICANN 2020

Publisher: Springer International Publishing

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Abstract

Neuromorphic vision sensors exhibit several advantages compared to conventional frame-based cameras including low latencies, high dynamic range, and low data rates. However, how efficient visual representations can be learned from the output of such sensors in an unsupervised fashion is still an open problem. Here we present a spiking neural network that learns spatio-temporal receptive fields in an unsupervised way from the output of a neuromorphic event-based vision sensor. Learning relies on the combination of spike timing-dependent plasticity with different synaptic delays, the homeostatic regulations of synaptic weights and firing thresholds, and fast inhibition among neurons to decorrelate their responses. Our network develops biologically plausible spatio-temporal receptive fields when trained on real world input and is suited for implementation on neuromorphic hardware.

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previous chapter Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware
next chapter Spike-Train Level Unsupervised Learning Algorithm for Deep Spiking Belief Networks
Literature
1.
Akolkar, H., Panzeri, S., Bartolozzi, C.: Spike time based unsupervised learning of receptive fields for event-driven vision. In: IEEE International Conference on Robotics and Automation. IEEE, May 2015
2.
3.
4.
Brandli, C., Berner, R., Yang, M., Liu, S.C., Delbruck, T.: A 240 \(\times \) 180 130 dB 3 \(\mu \)s latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49(10), 2333–2341 (2014)
5.
Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9 (2015)
6.
Haessig, G., Cassidy, A., Alvarez, R., Benosman, R., Orchard, G.: Spiking optical flow for event-based sensors using IBM’s TrueNorth neurosynaptic system. IEEE Trans. Biomed. Circuits Syst. 12(4), 860–870 (2018)CrossRef
7.
Hopkins, M., Pineda-García, G., Bogdan, P.A., Furber, S.B.: Spiking neural networks for computer vision. Interface Focus. 8(4), 20180007 (2018)CrossRef
8.
Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw. 99, 56–67 (2018)CrossRef
9.
Li, B., Peterson, M.R., Freeman, R.D.: Oblique effect: a neural basis in the visual cortex. J. Neurophysiol. 90(1), 204–217 (2003)CrossRef
10.
Orchard, G., Benosman, R., Etienne-Cummings, R., Thakor, N.V.: A spiking neural network architecture for visual motion estimation. In: IEEE Biomedical Circuits and Systems Conference (BioCAS), October 2013
11.
Paredes-Valles, F., Scheper, K.Y.W., Croon, G.C.H.E.D.: Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: from events to global motion perception. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2051–2064 (2018)
12.
Paulun, L., Wendt, A., Kasabov, N.: A retinotopic spiking neural network system for accurate recognition of moving objects using NeuCube and dynamic vision sensors. Front. Comput. Neurosci. 12, 13 Pages (2018). Article no: 42
13.
Perez-Carrasco, J.A., Serrano, C., Acha, B., Serrano-Gotarredona, T., Linares-Barranco, B.: Spike-based convolutional network for real-time processing. In: IAPR International Conference on Pattern Recognition. IEEE, August 2010
14.
Rebecq, H., Gehrig, D., Scaramuzza, D.: ESIM: an open event camera simulator. In: Conference on Robot Learning, October 2018
15.
Rothkopf, C.A., Weisswange, T.H., Triesch, J.: Learning independent causes in natural images explains the spacevariant oblique effect. ICDL 2009. IEEE 8th International Conference on Development and Learning, pp. 1–6, January 2009
16.
Stromatias, E., Soto, M., Serrano-Gotarredona, T., Linares-Barranco, B.: An event-driven classifier for spiking neural networks fed with synthetic or dynamic vision sensor data. Front. Neurosci. 11, 17 Pages (2017). Article no: 350
17.
Tien, N.W., Kerschensteiner, D.: Homeostatic plasticity in neural development. Neural Dev.13(1), 7 Pages (2018)
18.
Triesch, J., Vo, A.D., Hafner, A.S.: Competition for synaptic building blocks shapes synaptic plasticity. Elife 7, e37836 (2018)CrossRef
19.
20.
Zhao, B., Ding, R., Chen, S., Linares-Barranco, B., Tang, H.: Feedforward categorization on AER motion events using cortex-like features in a spiking neural network. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 1963–1978 (2015)MathSciNetCrossRef
Metadata
Title
Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor
Authors
Thomas Barbier
Céline Teulière
Jochen Triesch
Copyright Year
2020
Book
Artificial Neural Networks and Machine Learning – ICANN 2020

Print ISBN: 978-3-030-61615-1

Electronic ISBN: 978-3-030-61616-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-61616-8_50

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